@inproceedings{e99714801c6c41eaa295d16185649f9b,
title = "A ν -Net: Automatic Detection and Segmentation of Aneurysm",
abstract = "We propose an automatic solution for the CADA 2020 challenge to detect aneurysm from Digital Subtraction Angiography (DSA) images. Our method relies on 3D U-net as the backbone and heavy data augmentation with a carefully chosen loss function. We were able to generalize well using our solution (despite training on a small dataset) that is demonstrated through accurate detection and segmentation on the test data.",
keywords = "Aneurysm, Detection, Segmentation",
author = "Suprosanna Shit and Ivan Ezhov and Paetzold, {Johannes C.} and Bjoern Menze",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st Cerebral Aneurysm Detection and Analysis challenge, CADA 2020 held in Conjunction with 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 ; Conference date: 08-10-2020 Through 08-10-2020",
year = "2021",
doi = "10.1007/978-3-030-72862-5_5",
language = "English",
isbn = "9783030728618",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "51--57",
editor = "Anja Hennemuth and Leonid Goubergrits and Matthias Ivantsits and Jan-Martin Kuhnigk",
booktitle = "Cerebral Aneurysm Detection - First Challenge, CADA 2020, Held in Conjunction with MICCAI 2020, Proceedings",
}